ArticleBMC public health2019
Validation of automatic wear-time detection algorithms in a free-living setting of wrist-worn and hip-worn ActiGraph GT3X.
Article in BMC public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Technology for monitoring everyday prosthesis use: a systematic review.Journal of neuroengineering and rehabilitation · 2020Pooled it
- Examining the Influence of Promoting Physical Activity Bouts on Adolescent Movement Dispersion: Secondary Analysis of a Randomized Control Trial.Journal for specialists in pediatric nursing : JSPN · 2026Trial
- Head Injury Treatment With Healthy and Advanced Dietary Supplements: A Pilot Randomized Controlled Trial of the Tolerability, Safety, and Efficacy of Branched Chain Amino Acids in the Treatment of Concussion in Adolescents and Young Adults.Journal of neurotrauma · 2024Trial
- Gamma frequency sensory stimulation in mild probable Alzheimer's dementia patients: Results of feasibility and pilot studies.PloS one · 2022Trial
- Effectiveness of an mHealth Application for Physical Activity Promotion Among Thai Older Adults: A Randomized Controlled Trial.Inquiry : a journal of medical care organization, provision and financingTrial
- Physical Activity Profile of School-Aged Children Born Very Preterm and Its Association With Sleep.Acta paediatrica (Oslo, Norway : 1992) · 2026Article
- Optimizing accelerometer implementation in a gerotherapeutic trial: Feasibility, adherence, and operational insights of ABLE.PLOS digital health · 2026Article
- The influence of job stress, sleep, physical activity, professional identity, and peer support on job satisfaction among emergency department nurses: a descriptive cross-sectional study based on Roy's adaptation model.Journal of Korean biological nursing science · 2026Article
- Cleaning and pre-processing of actigraphy data for physical activity and sleep research: a scoping review.Physiological measurement · 2026Article
- Article
- Implementation Framework to Increase Patient Adherence in Clinical Trials Utilizing Wearable Devices.Research square · 2025Article
- Semi-Automated Processing of Harmonized Accelerometer and GPS Data in R:Sensors (Basel, Switzerland) · 2025Article
- Exploring the Endorsement of Gender Stereotypes and Physical Activity in Young Women.Women's health reports (New Rochelle, N.Y.) · 2025Article
- Objectively Measured Physical Activity among People with and without HIV in Uganda: Associations with Cardiovascular Risk and Coronary Artery Disease.Global heart · 2025Article
- Accuracy of the Huawei GT2 Smartwatch for Measuring Physical Activity and Sleep Among Adults During Daily Life: Instrument Validation Study.JMIR formative research · 2024Article
- Interplay of race and neighborhood deprivation on resting and ambulatory blood pressure in young adults.American journal of physiology. Heart and circulatory physiology · 2024Article
- Objectively measured physical activity among people with and without HIV in Uganda: associations with cardiovascular risk and coronary artery disease.medRxiv : the preprint server for health sciences · 2024Article
- Transforming Big Data into AI-ready data for nutrition and obesity research.Obesity (Silver Spring, Md.) · 2024Review
- Effects of physical activity levels on characteristic pain in temporomandibular dysfunctions: a cross-sectional study.Head & face medicine · 2024Observational
- An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study.JMIR biomedical engineering · 2023Article
Corrections and comments
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWrist-worn accelerometers are increasingly used in epidemiological studies to record physical activity. The accelerometer data are usually only analyzed if the convention for compliant wear time is met (i.e. ≥ 10 h per day) but the algorithms to detect wear time have been developed based on data from hip-worn devices only and have not been tested in a free-living setting. The aim of this study was to validate the automatic wear time detection algorithms of one of the most frequently used devices in a free-living setting.
methodsSixty-eight adults wore one ActiGraph GT3X+ accelerometer on the wrist and one on the hip and additionally recorded wear times for each device separately in a diary. Monitoring phase was during three consecutive days in a free-living setting. Wear time was computed by the algorithms of Troiano and Choi and compared to the diary recordings.
resultsMean wear time was over 1420 min per day for both devices on all days. Lin's concordance correlation coefficient for the wrist-worn wear time was 0.73 (0.60; 0.82) when comparing the diary with Troiano and 0.78 (0.67; 0.86) when comparing the diary with Choi. For hip-worn devices the respective values were 0.23 (0.13; 0.33) for Troiano and 0.92 (0.88; 0.95) for Choi. Mean and standard deviation values for absolute percentage errors for wrist-worn devices were - 1.3 ± 8.1% in Troiano and 0.9 ± 7.7% in Choi. The respective values for hip-worn devices were - 17.5 ± 10% in Troiano and - 0.8 ± 4.6% in Choi.
conclusionsHip worn devices may be preferred due to their higher accuracy in physical activity measurement. Automatic wear-time detection can show high errors in individuals, but on a group level, type I, type II, and total errors are generally low when the Choi algorithm is used. In a real-life setting and participants with a high compliance, the algorithm by Choi is sufficient to distinguish wear time from non-wear time on a group level.
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